Abstract: Aspects of the disclosure are directed to an approach for extending forecasting models to various levels of granularity. The approach can include receiving a target level of granularity for distributing a forecast, performing forecast modeling at an aggregated level of granularity, and determining a distribution method to distribute results of the forecast model at the target level of granularity. The approach can improve performance over existing forecasting models with minimal overhead.

Abstract: A method for optimal time-to-event (TTE) modeling includes obtaining a forecast request requesting performance of a TTE forecast forecasting an amount of time an event will occur after a starting point in time. The method includes obtaining a cutoff value representing an amount of time after the starting point in time that the event has not occurred. The method also includes forecasting, using an uncertainty forecasting model, the amount of time the event will occur after the starting point in time and updating the forecasted amount of time based on the cutoff value. The method also includes returning the updated forecasted amount of time the event will occur after the starting point in time.

Abstract: Systems and techniques for directing data collection. Upon an initial data collection, the uncertainty of all or of a portion or portions of the collected data is evaluated. The collected data may be associated with a region, with portions of the collected data associated with subregions. Further data collection, including changes to or refinement of collection techniques, is undertaken based on evaluations of the uncertainty. Further data collection may be undertaken only for portions of the data for which uncertainty exceeds a threshold. Uncertainty evaluation may be performed at least in part using a model. The model may be an initial hypothesis model, and the model may be optimized as further data is collected, and the optimized model may be used to guide further data collection techniques, with iterations of data collection and model optimization being carried out concurrently.

Abstract: Systems and techniques for directing data collection. Upon an initial data collection, the uncertainty of all or of a portion or portions of the collected data is evaluated. The collected data may be associated with a region, with portions of the collected data associated with subregions. Further data collection, including changes to or refinement of collection techniques, is undertaken based on evaluations of the uncertainty. Further data collection may be undertaken only for portions of the data for which uncertainty exceeds a threshold. Uncertainty evaluation may be performed at least in part using a model. The model may be an initial hypothesis model, and the model may be optimized as further data is collected, and the optimized model may be used to guide further data collection techniques, with iterations of data collection and model optimization being carried out concurrently.

Abstract: Systems and techniques for computational load balancing. A problem space is partitioned into subspaces and the subspaces are assigned to processing nodes. The load of nodes associated with outer subspaces is compared with the load of nodes associated with inner subspaces, and partition boundary adjustments are made based on the relative loads of outer versus inner subspaces.

Abstract: Systems and techniques for computational load balancing. A problem space is partitioned into subspaces and the subspaces are assigned to processing nodes. The load of nodes associated with outer subspaces is compared with the load of nodes associated with inner subspaces, and partition boundary adjustments are made based on the relative loads of outer versus inner subspaces.

Abstract: Systems and techniques for directing data collection. Upon an initial data collection, the uncertainty of all or of a portion or portions of the collected data is evaluated. The collected data may be associated with a region, with portions of the collected data associated with subregions. Further data collection, including changes to or refinement of collection techniques, is undertaken based on evaluations of the uncertainty. Further data collection may be undertaken only for portions of the data for which uncertainty exceeds a threshold. Uncertainty evaluation may be performed at least in part using a model. The model may be an initial hypothesis model, and the model may be optimized as further data is collected, and the optimized model may be used to guide further data collection techniques, with iterations of data collection and model optimization being carried out concurrently.

Abstract: Systems and techniques for computational load balancing. A problem space is partitioned into subspaces and the subspaces are assigned to processing nodes. The load of nodes associated with outer subspaces is compared with the load of nodes associated with inner subspaces, and partition boundary adjustments are made based on the relative loads of outer versus inner subspaces.

Abstract: Systems and techniques for directing data collection. Upon an initial data collection, the uncertainty of all or of a portion or portions of the collected data is evaluated. The collected data may be associated with a region, with portions of the collected data associated with subregions. Further data collection, including changes to or refinement of collection techniques, is undertaken based on evaluations of the uncertainty. Further data collection may be undertaken only for portions of the data for which uncertainty exceeds a threshold. Uncertainty evaluation may be performed at least in part using a model. The model may be an initial hypothesis model, and the model may be optimized as further data is collected, and the optimized model may be used to guide further data collection techniques, with iterations of data collection and model optimization being carried out concurrently.

Abstract: Systems and techniques for computational load balancing. A problem space is partitioned into subspaces and the subspaces are assigned to processing nodes. The load of nodes associated with outer subspaces is compared with the load of nodes associated with inner subspaces, and partition boundary adjustments are made based on the relative loads of outer versus inner subspaces.

Abstract: Techniques are disclosed for parallel computing of a line of sight (LoS) map (e.g., view-shed) in a parallel computing system. For example, a method for computing an LoS map comprises the following steps. Data representing at least one image is obtained. An observation point in the at least one image is identified. A portion of the data that is associated with a given area in the image is partitioned into a plurality of sub-areas. The plurality of sub-areas are assigned to a plurality of processor elements of a parallel computing system, respectively, such that the data associated with each one of the plurality of sub-areas is processed independent from the data associated with each other of the plurality of sub-areas, wherein results of the processing by the processor elements represents the LoS map. The parallel computing system may be a multicore processor.

Abstract: Techniques are disclosed for parallel computing of a line of sight (LoS) map (e.g., view-shed) in a parallel computing system. For example, a method for computing an LoS map comprises the following steps. Data representing at least one image is obtained. An observation point in the at least one image is identified. A portion of the data that is associated with a given area in the image is partitioned into a plurality of sub-areas. The plurality of sub-areas are assigned to a plurality of processor elements of a parallel computing system, respectively, such that the data associated with each one of the plurality of sub-areas is processed independent from the data associated with each other of the plurality of sub-areas, wherein results of the processing by the processor elements represents the LoS map. The parallel computing system may be a multicore processor.

Abstract: A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Abstract: The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.

Abstract: A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word. Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Abstract: In one aspect of the present invention, page breaks are identified in the following manner. A set of ink data and a document description are processed by a variety of scoring methods, each of which generates a score for each possible insertion point in the ink. These scores are combined to produce a ranked list of hypothesized page breaks for the corresponding ink data. This ranked list is then used either to insert page breaks automatically using a predefined threshold to determine a cut-off in the list; or to present, on-line, to a human for verification/approval; or a mixture of the two based on two thresholds: one for automatic insertion and the other for human verification. It is to be understood not all scoring methods need be used, that is, one or more of the scoring methods may be used as needed.

Abstract: Several methods, and related apparatus, are provided for the entry of formatted ink data (i.e., electronic ink) such that individual items in the data may be parsed and recognized more effectively. Each method allows users to enter formatted ink data in-line, which can then be recognized with constraints and parsed for use in other application programs or databases. In addition, a method is provided for allowing user-specialization of any of these entry methods (or other similar) methods. Note that in any of these methods, the user may send the formatted ink either to the default ink-processing application, or else directly to another application or database.

Abstract: The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.

Abstract: A handwritten word is transcribed into a list of possibly correct transcriptions of the handwritten word. The list contains a number of text words, and this list is compared with previously stored set of lists of text words. Based on a metric, one or more nearest neighbor lists are selected from the set. A decision is made, according to a number of combination rules, as to which text word in the nearest neighbor lists or the recently transcribed list is the best transcription of the handwritten word. This best transcription is selected as the appropriate text word transcription of the handwritten word. The selected word is compared to a true transcription of the selected word. Machine learning techniques are used when the selected and true transcriptions differ. The machine learning techniques create or update rules that are used to determine which text word of the nearest neighbor lists or the recently transcribed list is the correct transcription of the handwritten word.

Abstract: Several methods, and related apparatus, are provided for the entry of formatted ink data (i.e., electronic ink) such that individual items in the data may be parsed and recognized more effectively. Each method allows users to enter formatted ink data in-line, which can then be recognized with constraints and parsed for use in other application programs or databases. In addition, a method is provided for allowing user-specialization of any of these entry methods (or other similar) methods. Note that in any of these methods, the user may send the formatted ink either to the default ink-processing application, or else directly to another application or database.